Introduction
The consulting world, a $250 billion industry, has long been a bastion of manual labor. Even as artificial intelligence has surged through law firms and accounting practices, the day‑to‑day work of consultants remains dominated by spreadsheets, PowerPoint decks, and endless data wrangling. In this context, London‑based Ascentra Labs announced a modest $2 million seed round led by Berlin‑based venture capital firm NAP. The startup, founded by former McKinsey consultants, is betting that a narrow focus on automating survey analysis for private‑equity due diligence can unlock a market that has stubbornly resisted digital transformation.
The story behind Ascentra is one of insider frustration. Paritosh Devbhandari, the company’s co‑founder and chief executive, spent years at McKinsey’s QuantumBlack, the firm’s AI and advanced analytics arm. He witnessed first‑hand how consultants, even at the most prestigious firms, would spend countless hours manually encoding survey responses from customers, suppliers, and market participants into Excel. The workflow was repetitive, error‑prone, and, most importantly, time‑consuming. Devbhandari’s insight was simple: if the most tedious part of a consultant’s job could be automated, the entire industry could become more efficient.
Ascentra’s platform ingests raw survey data files and outputs fully formatted Excel workbooks complete with traceable formulas—deliverables that a junior associate would normally spend hours building. By addressing a pain point that is both ubiquitous and under‑served, the startup aims to create a “beachhead” in a market that has proven resistant to broader AI solutions.
Main Content
The Gap Between Expectation and Reality
Despite the hype around generative AI, the consulting sector has been slow to adopt new technology. Devbhandari notes that the top of the funnel is crowded with pitches, yet very few solutions actually penetrate the firm’s procurement process. Professional services firms demand extensive security credentials and customer references before even allowing a pilot, and this gatekeeping often leads to the failure of many startups.
Beyond the sales cycle, consulting presents unique technical challenges. Unlike legal work, which largely revolves around text documents that large language models can process, consulting workflows involve multiple data modalities—Excel spreadsheets, PowerPoint decks, Word documents—each with its own structure and semantics. A single project might contain dozens of distinct Excel files, each with different column names, formulas, and formatting conventions. This heterogeneity makes it difficult for a single AI agent to handle the entire workflow.
A Narrow Focus on Private‑Equity Survey Analysis
Ascentra’s strategy is to solve a single, well‑defined problem: automating survey analysis for private‑equity due diligence. Private‑equity work is more standardized than other consulting engagements, with recurring analyses across deals. This repeatability makes automation feasible and reduces the risk of model hallucinations—a critical concern when a single spreadsheet error could influence a billion‑dollar investment decision.
The company claims that three of the world’s top five consulting firms now use its platform, reporting time savings of 60–80 percent on active due diligence projects. While the startup cannot publicly name its clients due to the private nature of the industry, the fact that large firms have adopted the solution is a strong validation of its niche focus.
Managing Accuracy and Hallucinations
Accuracy is paramount in quantitative consulting. As Devbhandari explains, consultants will revert to Excel if they perceive any risk of error. To address this, Ascentra limits the role of AI to data ingestion and interpretation. The actual analysis is performed by deterministic Python scripts that produce consistent, verifiable outputs. The platform then converts these outputs into live, traceable Excel formulas, allowing consultants to audit the calculations step by step.
This hybrid approach—AI for data ingestion, deterministic code for analysis—aims to strike a balance between speed and reliability. By keeping the core analytical engine deterministic, the startup reduces the risk of hallucinations that plague many generative models.
Enterprise Security and Compliance
Selling to large consulting firms requires meeting stringent security standards. Ascentra has proactively obtained SOC 2 Type II and ISO 27001 certifications, and it is currently under audit for ISO 42001, a standard for AI management systems. The company’s data handling policies are rigorous: client data is deleted within 30–45 days, and the platform does not use customer data to train its models.
Survey data, while still sensitive, is less confidential than financial statements or proprietary client data. This relative lower sensitivity may make it easier for Ascentra to navigate the regulatory landscape, but the company’s certifications demonstrate its commitment to enterprise‑grade security.
Pricing That Aligns With Consulting Budgets
Unlike many SaaS vendors that rely on subscription models, Ascentra charges on a per‑project basis. This pricing structure mirrors how consulting firms allocate budgets—central budgets cover shared tools, while project budgets fund specific resources. By aligning its pricing with the way firms spend money, Ascentra lowers the barrier to entry and reduces the need for central IT procurement approval.
However, per‑project pricing introduces revenue unpredictability. The company’s success will depend on converting pilot wins into broader enterprise contracts, a challenge that Devbhandari acknowledges.
The Future of Consulting Work
Devbhandari is clear that AI will not eliminate consulting jobs; rather, it will transform the nature of the work. He argues that the role of consultants will shift from manual data wrangling to higher‑level strategic analysis, with AI handling the repetitive tasks. While the exact impact on employment remains uncertain, the industry is poised for a fundamental shift.
Ascentra’s seed funding will primarily support expansion into the United States, where the majority of its customers already operate. The startup plans to build out a go‑to‑market team in the U.S., leveraging the country’s appetite for innovation and its concentration of consulting firms.
Conclusion
Ascentra Labs’ $2 million seed round represents a calculated bet on a niche that has long been overlooked by broader AI solutions. By focusing on the highly repetitive task of survey analysis in private‑equity due diligence, the startup has carved out a defensible position in a market that values precision, security, and repeatable workflows. The company’s hybrid AI approach, combined with enterprise‑grade security certifications and a pricing model that aligns with consulting budgets, positions it well to convert pilot wins into lasting contracts.
The journey ahead will be challenging. Scaling beyond pilot projects, navigating the complex procurement processes of large firms, and defending against well‑funded competitors will test Ascentra’s resilience. Yet the company’s clear focus, technical rigor, and deep industry expertise give it a promising chance to disrupt an industry that has resisted digital transformation for decades.
Call to Action
If you are a consultant, a private‑equity professional, or a technology investor interested in the next wave of AI in professional services, keep an eye on Ascentra Labs. Their approach to automating the most tedious part of consulting work could redefine productivity across the industry. For consulting firms looking to reduce spreadsheet fatigue, consider exploring Ascentra’s platform as a pilot solution. And for investors, the company’s focused niche and early traction in top firms signal a potentially high‑impact opportunity in the AI‑in‑business space.